Mining Noise-Tolerant Frequent Closed Itemsets in Very Large Database
نویسندگان
چکیده
منابع مشابه
Mining Frequent Itemsets in Evidential Database
Mining frequent patterns is widely used to discover knowledge from a database. It was originally applied on Market Basket Analysis (MBA) problem which represents the Boolean databases. In those databases, only the existence of an article (item) in a transaction is defined. However, in real-world application, the gathered information generally suffer from imperfections. In fact, a piece of infor...
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Extracting frequent itemsets is an important task in many data mining applications. When data are very large, it becomes mandatory to perform the mining task by using an external memory algorithm, but only a few of these algorithms have been proposed so far. Since also the result set of all the frequent itemsets is likely to be undesirably large, condensed representations, such as closed itemse...
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Closed itemsets are semantically equivalent to frequent itemsets but are orders of magnitude fewer, thus allowing the knowledge extracted from a transactional database to be represented very concisely. Unfortunately, no algorithm has been yet devised which allows to mine closed patterns directly. All existing algorithms may in fact generate the same closed itemset multiple times, and need to ma...
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Frequent itemsets mining is a classic problem in data mining and plays an important role in data mining research for over a decade. However, the mining of the all frequent itemsets will lead to a massive number of itemsets. Fortunately, this problem can be reduced to the mining of maximal frequent itemsets. In this paper, we propose a new method for mining maximal frequent itemsets. Our method ...
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ژورنال
عنوان ژورنال: IEICE Transactions on Information and Systems
سال: 2009
ISSN: 0916-8532,1745-1361
DOI: 10.1587/transinf.e92.d.1523